Deconvoluting prediabetes: A path to understanding the origins of complications
Authors: Robert Wagner
Journal: Cell Reports Medicine (2023)

A study of 56,000 people with prediabetes in China stratified the population into 6 clusters, among which were two clusters of impaired insulin secretion and a cluster with hyperinsulinemic insulin resistance.1Zheng R. Xu Y. Li M. Gao Z. Wang G. Hou X. Chen L. Huo Y. Qin G. Yan L. et al.Data-driven subgroups of prediabetes and the associations with outcomes in Chinese adults.Cell Rep. Med. 2023; 4100958https://doi.org/10.1016/j.xcrm.2023.100958Abstract Full Text Full Text PDF PubMed Scopus (1) Google Scholar A follow-up showed different risk of complications, demonstrating prediabetes heterogeneity. A study of 56,000 people with prediabetes in China stratified the population into 6 clusters, among which were two clusters of impaired insulin secretion and a cluster with hyperinsulinemic insulin resistance.1Zheng R. Xu Y. Li M. Gao Z. Wang G. Hou X. Chen L. Huo Y. Qin G. Yan L. et al.Data-driven subgroups of prediabetes and the associations with outcomes in Chinese adults.Cell Rep. Med. 2023; 4100958https://doi.org/10.1016/j.xcrm.2023.100958Abstract Full Text Full Text PDF PubMed Scopus (1) Google Scholar A follow-up showed different risk of complications, demonstrating prediabetes heterogeneity. Type 2 diabetes does not develop suddenly. It is preceded by a prolonged period of transition, characterized by varying diagnostic criteria for fasting glucose and HbA1c levels or an elevated post-glucose challenge after an oral glucose tolerance test. Understanding this stage of hyperglycemia is critical because by the time diabetes is diagnosed, around 30% of affected individuals already have complications.2Gedebjerg A. Almdal T.P. Berencsi K. Rungby J. Nielsen J.S. Witte D.R. Friborg S. Brandslund I. Vaag A. Beck-Nielsen H. et al.Prevalence of micro- and macrovascular diabetes complications at time of type 2 diabetes diagnosis and associated clinical characteristics: A cross-sectional baseline study of 6958 patients in the Danish DD2 cohort.J. Diabetes Complications. 2018; 32: 34-40https://doi.org/10.1016/j.jdiacomp.2017.09.010Crossref PubMed Scopus (71) Google Scholar Prediabetes itself has been linked to various complications of diabetes and its prevalence is alarmingly high in some populations, even though not all individuals will experience negative effects.3Schlesinger S. Neuenschwander M. Barbaresko J. Lang A. Maalmi H. Rathmann W. Roden M. Herder C. Prediabetes and risk of mortality, diabetes-related complications and comorbidities: Umbrella review of meta-analyses of prospective studies.Diabetologia. 2022; 65: 275-285https://doi.org/10.1007/s00125-021-05592-3Crossref PubMed Scopus (56) Google Scholar To effectively prevent diabetes and its consequences, it's important to identify those at risk and target interventions to those who need it most. In a large population-based study of people above 40 years of age that was conducted in multiple communities across China, one-third of all recruited people had prediabetes according to the broader criteria of the American Diabetes Association.1Zheng R. Xu Y. Li M. Gao Z. Wang G. Hou X. Chen L. Huo Y. Qin G. Yan L. et al.Data-driven subgroups of prediabetes and the associations with outcomes in Chinese adults.Cell Rep. Med. 2023; 4100958https://doi.org/10.1016/j.xcrm.2023.100958Abstract Full Text Full Text PDF PubMed Scopus (1) Google Scholar The researchers set out to investigate this prediabetic population of ∼56,000 people to identify clusters of similarities based on 12 variables reflecting anthropometric, glycemic, and other metabolic traits. By using multiple runs of k-means clustering with varying cluster counts on randomly selected subsets of the population and identifying pairs of individuals repeatedly being grouped together, they derived a consensus pairwise similarity matrix for the whole study population, which led to the identification of an optimum segregation of 6 clusters (Figure 1). These 6 clusters were denominated in ascending order by diabetes risk, with cluster 6 (20% of the cohort) exhibiting the highest risk to develop diabetes. While this cluster had unsurprisingly the highest levels of fasting glucose, post-challenge glucose, and HbA1c, body-mass index was higher in another cluster, cluster 4. Cluster 4, comprising 13% of the population, also featured the highest insulin resistance as measured by HOMA-IR with a compensatory elevation of insulin secretion (HOMA-B). Follow-up data were available in ∼37,000–4,7000 individuals for three endpoints: diabetes, chronic kidney disease (CKD), and cardiovascular disease (CVD) with a median follow-up time of around 3 years. The highest risk of CKD was observed for cluster 4, and this was the only cluster showing a significantly higher risk of CVD compared with the cluster with the lowest risk. CKD risk was also elevated for cluster 1 and cluster 6. Cluster 1 showed the lowest progression to diabetes and had accordingly low fasting and post-challenge glucose but mostly comprised participants with elevated HbA1c over 5.7%. Whether this constellation of relatively high HbA1c, low glucose, and increased CKD risk without baseline differences in estimated glomerular filtration rate are due to an intrinsic bias of hemoglobin glycation in a specific subgroup of a South-East Asian population or explainable by a preanalytical or analytical bias in the large study is unclear. When comparing this work to our study that investigated a precisely phenotyped but considerably smaller population of mostly Central-European ancestry, there are several similarities and differences.4Wagner R. Heni M. Tabák A.G. Machann J. Schick F. Randrianarisoa E. Hrabě de Angelis M. Birkenfeld A.L. Stefan N. Peter A. et al.Pathophysiology-based subphenotyping of individuals at elevated risk for type 2 diabetes.Nat. Med. 2021; 27: 49-57https://doi.org/10.1038/s41591-020-1116-9Crossref PubMed Scopus (157) Google Scholar While the work of Zheng et al. only studied people with strictly defined prediabetes, the European study recruited individuals with a clinically elevated diabetes risk, thus yielding an enrichment of prediabetes to ∼50% of the studied population but reflecting the whole metabolic spectrum preceding diabetes. Despite an inherent arbitrariness in the determination of the optimal cluster count, both studies identified 6 data-driven clusters. Both works pinpointed a group, cluster 4 in the work of Zheng et al. and cluster 6 in our analysis, that is characterized by mild hyperglycemia, insulin resistance, hyperinsulinemia, and elevated CKD risk. This cluster does not have a very high risk of diabetes progression, but once it progresses, most individuals progress to a severe insulin-resistant endotype of diabetes.4Wagner R. Heni M. Tabák A.G. Machann J. Schick F. Randrianarisoa E. Hrabě de Angelis M. Birkenfeld A.L. Stefan N. Peter A. et al.Pathophysiology-based subphenotyping of individuals at elevated risk for type 2 diabetes.Nat. Med. 2021; 27: 49-57https://doi.org/10.1038/s41591-020-1116-9Crossref PubMed Scopus (157) Google Scholar,5Ahlqvist E. Storm P. Käräjämäki A. Martinell M. Dorkhan M. Carlsson A. Vikman P. Prasad R.B. Aly D.M. Almgren P. et al.Novel subgroups of adult-onset diabetes and their association with outcomes: A data-driven cluster analysis of six variables.Lancet Diabetes Endocrinol. 2018; 6: 361-369https://doi.org/10.1016/S2213-8587(18)30051-2Abstract Full Text Full Text PDF PubMed Scopus (1161) Google Scholar Recent findings suggest that hyperinsulinemia in this prediabetes endotype may be a primary phenomenon leading to insulin resistance, as it appears to be refractory to a lifestyle intervention.6Fritsche A. Wagner R. Heni M. Kantartzis K. Machann J. Schick F. Lehmann R. Peter A. Dannecker C. Fritsche L. et al.Different effects of lifestyle intervention in high- and low-risk prediabetes: Results of the randomized controlled Prediabetes Lifestyle Intervention Study (PLIS).Diabetes. 2021; 70: 2785-2795https://doi.org/10.2337/db21-0526Crossref PubMed Scopus (25) Google Scholar,7Wagner R. Heni M. Kantartzis K. Sandforth A. Machann J. Schick F. Peter A. Fritsche L. Szendrödi J. Pfeiffer A.F.H. et al.Lower hepatic fat is associated with improved insulin secretion in a high-risk prediabetes subphenotype during lifestyle intervention.Diabetes. 2023; 72: 362-366https://doi.org/10.2337/db22-0441Crossref PubMed Scopus (2) Google Scholar In the work of Zheng et al., the two clusters with the top diabetes progression rates, clusters 5 and 6, seem to be separated by a hepatic phenotype rather than the contrast of low versus high insulin secretion. Of note, low insulin secretion with only moderate insulin resistance mostly dominates the diabetes endotypes in South-East Asian populations, as shown in multiple studies attempting to replicate the diabetes clusters established by Ahlqvist et al. in India and China.8Ke C. Narayan K.M.V. Chan J.C.N. Jha P. Shah B.R. Pathophysiology, phenotypes and management of type 2 diabetes mellitus in Indian and Chinese populations.Nat. Rev. Endocrinol. 2022; 18: 413-432https://doi.org/10.1038/s41574-022-00669-4Crossref PubMed Scopus (33) Google Scholar Interestingly, in the prediabetes cluster analysis of Zheng et al., cluster 5 with the highest transaminase levels showed the lowest cardiovascular risk. This study delivers valuable insights into the heterogeneity of prediabetes in a large South-East Asian study population but also has important limitations. Clustering approaches generate groups based on similarity of the involved variables. Many metabolic variables have high intraindividual variances. With increasing variable and cluster count, cluster assignments tend to be unstable. This is reflected by a low intraindividual concordance of cluster re-assignments in the study by Zheng et al., when the clustering process was repeated at a later time point. Of note, repeated data were available only in a small subset of the study, which could have biased the results. Follow-up time was short, and there were only a limited number of complications surveyed. Also, the study lacks an independent validation. Deconvolution of the blend of metabolic alterations characterizing the prediabetes-diabetes spectrum is one important tool to identify the origins of these mixed phenotypes. In addition to a better mechanistic understanding, these methods could create early windows of opportunities for targeted preventive measures to extend precision medicine to the prediabetes-diabetes continuum.9Herder C. Roden M. A novel diabetes typology: towards precision diabetology from pathogenesis to treatment.Diabetologia. 2022; 65: 1770-1781https://doi.org/10.1007/s00125-021-05625-xCrossref PubMed Scopus (36) Google Scholar R.W. reports lecture fees from Novo Nordisk, Sanofi, and Eli Lilly. R.W. served on an advisory board for Akcea Therapeutics, Daiichi Sankyo, Sanofi, Eli Lilly, and NovoNordisk. Data-driven subgroups of prediabetes and the associations with outcomes in Chinese adultsZheng et al.Cell Reports MedicineMarch 1, 2023In BriefZheng et al. use data-driven clustering approaches to confirm the heterogeneity in individuals with prediabetes and explore their associations with major diseases. Individuals with prediabetes differ in metabolic features and risks of disease progression, raising the possibility of a practical, stratified approach for the prevention of diabetes and related diseases. Full-Text PDF Open Access